We develop robust targeted maximum likelihood estimators (TMLE) for transporting intervention effects from one population to another. Specifically, we develop TMLE estimators for three transported estimands: intent-to-treat average treatment effect (ATE) and complier ATE, which are relevant for encouragement-design interventions and instrumental variable analyses, and the ATE of the exposure on the outcome, which is applicable to any randomized or observational study. We demonstrate finite sample performance of these TMLE estimators using simulation, including in the presence of practical violations of the positivity assumption. We then apply these methods to the Moving to Opportunity trial, a multi-site, encouragement-design intervention in which families in public housing were randomized to receive housing vouchers and logistical support to move to low-poverty neighborhoods. This application sheds light on whether effect differences across sites can be explained by differences in population composition.

中文翻译：

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对跨站点传输的鼓励设计干预效果的稳健估计。


我们开发了强大的目标最大似然估计器（TMLE），用于将干预效果从一个群体转移到另一个群体。具体来说，我们为三个传输估计量开发 TMLE 估计量：意向治疗平均治疗效果 (ATE) 和编译者 ATE，它们与鼓励设计干预措施和工具变量分析相关，以及结果暴露的 ATE，其中适用于任何随机或观察性研究。我们使用仿真演示了这些 TMLE 估计器的有限样本性能，包括在实际违反积极性假设的情况下。然后，我们将这些方法应用于“转向机会”试验，这是一项多地点、鼓励设计的干预措施，其中公共住房中的家庭被随机分配以获得住房券和后勤支持，以搬到低贫困社区。该应用揭示了不同地点的影响差异是否可以通过人口构成的差异来解释。